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75 lines
2.9 KiB
Markdown
75 lines
2.9 KiB
Markdown
---
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id: "lfm-kernel-code-review"
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title: "Review existing LFM kernel implementations"
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status: "In Progress"
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priority: "High"
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created: "2025-08-15"
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last_updated: "2025-08-15"
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owner: "Neil Lawrence"
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github_issue: ""
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dependencies: ""
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tags:
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- lfm
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- kernel
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- code-review
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- documentation
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---
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# Review existing LFM kernel implementations
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## Description
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Conduct a comprehensive review of existing LFM (Latent Force Model) kernel implementations in both GPy and MATLAB to understand the current state, design decisions, and limitations.
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## Background
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- GPy has existing ODE-based kernels (`EQ_ODE1`, `EQ_ODE2`) that implement LFM concepts
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- MATLAB implementation in GPmat provides a more complete LFM framework
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- Need to understand differences and identify modernization opportunities
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## Tasks
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- [x] Review `GPy/kern/src/eq_ode1.py` and `eq_ode2.py` implementations
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- [x] Analyze MATLAB LFM implementation structure and patterns
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- [ ] Document current limitations and inconsistencies
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- [ ] Identify reusable components and design patterns
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- [ ] Compare parameter handling approaches
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- [ ] Review cross-kernel computation methods
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- [ ] Document mathematical foundations and implementation details
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## Acceptance Criteria
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- [ ] Complete documentation of existing implementations
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- [ ] Clear understanding of design differences between GPy and MATLAB versions
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- [ ] Identified list of modernization opportunities
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- [ ] Documentation of mathematical foundations
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- [ ] Assessment of current limitations and bugs
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## Implementation Notes
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- Focus on understanding the mathematical foundations from the papers
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- Pay attention to parameter tying and multi-output handling
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- Document the differential equation structure and kernel computation
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- Identify opportunities for using GPy's modern multioutput kernel approach
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## Related
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- CIP: 0001 (LFM kernel implementation)
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- Papers: Álvarez et al. (2009, 2012), Lawrence et al. (2006)
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## Progress Updates
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### 2025-08-15
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Started code review task. Initial findings:
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**GPy Implementations:**
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- `EQ_ODE1`: First-order differential equation kernel with decay rates and sensitivities
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- `EQ_ODE2`: Second-order differential equation kernel with spring/damper constants
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- Both use GPy's multioutput approach with output index as second input dimension
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- Complex kernel computation with multiple covariance types (Kuu, Kfu, Kuf, Kusu)
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- Uses `@Cache_this` decorator for performance optimization
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**MATLAB Implementation:**
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- More complete framework with `lfmCreate`, `lfmKernCompute`, `lfmKernParamInit`
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- Uses multi-kernel approach with parameter tying
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- Supports multiple displacements driven by multiple forces
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- Cleaner separation of concerns with dedicated model creation
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**Key Differences:**
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- GPy uses single kernel class per ODE order, MATLAB uses multi-kernel composition
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- GPy has more complex index handling for multioutput
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- MATLAB has better parameter organization and tying mechanisms
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